Chapter

Semantic Hyper/Multimedia Adaptation

Volume 418 of the series Studies in Computational Intelligence pp 83-125

Vision Based Semantic Analysis of Surveillance Videos

  • Virginia Fernandez ArguedasAffiliated withMultimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary, University of London Email author 
  • , Qianni ZhangAffiliated withMultimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary, University of London
  • , Krishna ChandramouliAffiliated withMultimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary, University of London
  • , Ebroul IzquierdoAffiliated withMultimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary, University of London

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Abstract

As recent research in automatic surveillance systems has attracted many cross-domain researchers, a large-number of algorithms have been proposed for automating surveillance systems. The objective of this chapter is twofold: First, we present an extensive survey of different techniques that have been proposed for surveillance systems categorised into motion analysis, visual feature extraction and indexing. Second, an integrated surveillance framework for unsupervised object indexing is developed to study and evaluate the performance of visual features. The study focuses on two characteristics highly related with human visual perception, colour and texture. The set of visual features under analysis comprises two categories, new leading visual features versus state-of-the-art MPEG-7 visual features. The evaluation of the framework is carried out with AVSS 2007 and CamVid 2008 datasets.